Uporedite metode
Pregledajte izabrane metode jednu pored druge; redovi koji se razlikuju su istaknuti.
| Varijacioni autoenkoder× | Autoenkoder× | |
|---|---|---|
| Oblast | Duboko učenje | Duboko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2014 | 2006 |
| Tvorac≠ | Kingma, D. P. & Welling, M. | Hinton, G.E. & Salakhutdinov, R.R. |
| Tip≠ | Deep generative latent-variable model (encoder–decoder) | Neural network (encoder-decoder) |
| Temeljni izvor≠ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ | Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. DOI ↗ |
| Drugi nazivi | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model | Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network |
| Srodne≠ | 5 | 4 |
| Sažetak≠ | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. | An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data. |
| ScholarGateSkup podataka ↗ |
|
|